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Collaborative prediction and ranking with non-random missing data

Published:23 October 2009Publication History

ABSTRACT

A fundamental aspect of rating-based recommender systems is the observation process, the process by which users choose the items they rate. Nearly all research on collaborative filtering and recommender systems is founded on the assumption that missing ratings are missing at random. The statistical theory of missing data shows that incorrect assumptions about missing data can lead to biased parameter estimation and prediction. In a recent study, we demonstrated strong evidence for violations of the missing at random condition in a real recommender system. In this paper we present the first study of the effect of non-random missing data on collaborative ranking, and extend our previous results regarding the impact of non-random missing data on collaborative prediction.

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                cover image ACM Conferences
                RecSys '09: Proceedings of the third ACM conference on Recommender systems
                October 2009
                442 pages
                ISBN:9781605584355
                DOI:10.1145/1639714

                Copyright © 2009 ACM

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                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 23 October 2009

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